Validating crowdsourced bicycling mobility data for supporting city planning

Abstract

Crowdsourced data hold great potential in creating metrics which can support city planning. Devices such as smartphones, with their sensor capabilities including GPS can be used to capture a wealth of mobility data at the scale of individual trips. However, the use of crowdsourced data for city planning is still hindered by doubts about their accuracy, objectivity and representativeness. This study proposes a validation process with five criteria – geographic coverage, origin-destination match, demographic match, distance–duration distributions, and route match – to assess different representativeness aspects of mobility data. The validation process is demonstrated using a crowdsourced data on bicycling in Sydney, Australia, compared with Census Journey to Work data. Results indicate a good overall fit of the sample against the population, but variations across the five criteria. Implications of these variations on the suitable uses for the crowdsourced data are discussed, and current limitations of the proposed approach are identified.

Keywords:

• Bicycling
• active transport
• crowdsourced data
• bias
• smart city

Acknowledgements

The authors would like to acknowledge Bicycle Network for supplying the RiderLog data, and the Australian National Data Services (ANDS) for funding the ‘High-Value Data Collection Project (2016–2017)’, focused on cleaning and validating RiderLog crowdsourced data. They also would like to thank the anonymous reviewers for their valuable revision of the text, comments and contributions to the improvement of this paper.